MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

TLDR

Many methods exist for brain MRI tissue segmentation, making it difficult to choose the best approach. The MRBrainS online evaluation framework was created to assess (semi)automatic gray matter, white matter, and cerebrospinal fluid segmentation algorithms on 3T MRI scans of elderly subjects. Participants run their algorithms on provided training and testing datasets, and the framework ranks results using Dice, H95, and AVD metrics against full manual segmentations, publishing outcomes on the MRBrainS13 website. The challenge evaluated eleven algorithms and three common freeware packages, demonstrating that the framework offers an objective comparison that helps identify the best performing method.

Abstract

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.